|Preprocessing of Configuration Space for Improved Sampling Based Path Planning - Titas Bera, M. Seetharama Bhat, D. Ghose|
Sampling based planners have been successful in path planning of robots with many degrees of freedom, but still remains ineective when the configuration space has a narrow passage. This paper presents two new techniques of preprocessing the configuration space. The first technique called a Random Walk to Surface (RWS), uses a random walk strategy to generate samples in narrow regions quickly, thus improving eciency of Probabilistic Roadmap (PRM) based planners...
Keywords: Robot Motion Planning; Randomized Algorithm; PRM; RRT
|Mobile Robot Navigation Using a Combined Optimized Potential Field and a Boundary Following Algorithm - Samer Charifa, Marwan Bikdash|
We propose a novel method to the navigation of mobile robots that combines a modified potential field method with a boundary-following algorithm. The resulting method avoids many of the pitfalls of each component method, such as entrapment in local minima, oscillation in narrow corridors, hugging obstacle boundaries inefficiently, and low-quality velocity and acceleration profiles. The proposed harmonic field has non-uniform boundary conditions based on the length of the shortest path to the tar...
Keywords: Motion Planning; Harmonic Potential Field; Line-segment Robot
|MIT 6.832 Underactuated Robotics, Spring 2009 - MIT OpenCourseWare|
Instructor: Russell Tedrake Robots today move far too conservatively, using control systems that attempt to maintain full control authority at all times. Humans and animals move much more aggressively by routinely executing motions which involve a loss of instantaneous control authority. Controlling nonlinear systems without complete control authority requires methods that can reason about and exploit the natural dynamics of our machines...
Keywords: underactuated robotics; actuated systems; nonlinear dynamics; simple pendulum; optimal control; double integrator; quadratic regulator; Hamilton-Jacobi-Bellman sufficiency; minimum time control; acrobot; cart-pole; partial feedback linearization; energy shaping; policy search; open-loop optimal control; trajectory stabilization; iterative linear quadratic regulator; differential dynamic programming; walking models; rimless wheel; compass gait; kneed compass gait; feedback control; running models; spring-loaded inverted pendulum; Raibert hoppers; motion planning; randomized motion planning; rapidly-exploring randomized trees; probabilistic road maps; feedback motion planning; planning with funnels; linear quadratic regulator; function approximation; state distribution dynamics; state estimation; stochastic optimal control; aircraft; swimming; flapping flight; randomized policy gradient; model-free value methods; temporarl difference learning; Q-learning; actor-critic methods
|Claude Latombe, Ming C. Lin - Algorithmic Foundations of Robotics IX - David Hsu, Volkan Isler, Jean|
Claude Latombe, Ming C. Lin - Algorithmic Foundations of Robotics IX
Keywords: path; algorithm; robot; planning; robotics; algorithms; robots; trajectory; collision; paths; motion planning; path planning; local planning; configuration space; international conference; algorithmic foundations; bayes tree; collision detection; robot motion; vector field
|mit :: ai :: aim :: AIM-957|
From the bitsavers.org collection, a scanned-in computer-related document.mit :: ai :: aim :: AIM-957
Keywords: path; voronoi; homotopy; configuration; define; critical; constraints; diagram; deformation; predicate; free space; finite number; voronoi diagram; motion planning; path segment; lowest common; common ancestor; simplified voronoi; voronoi diagrams; configuration space
|Makoto Kaneko, Yoshihiko Nakamura - Robotics Research|
Makoto Kaneko, Yoshihiko Nakamura - Robotics Research
Keywords: robot; robotics; robots; object; visual; motion; algorithm; control; ieee; data; motion planning; international conference; robot hand; verlag berlin; berlin heidelberg; loop closure; intelligent robots; force feedback; computer vision; motion patterns
|mit :: ai :: aim :: AIM-896|
From the bitsavers.org collection, a scanned-in computer-related document.mit :: ai :: aim :: AIM-896
Keywords: link; obstacle; dimensional; manipulator; configuration; contact; joint; vertex; edge; ranges; orientation constraint; three dimensional; free space; motion planning; slice projection; slice projections; link edge; configuration space; dimensional slice; position vector
|mit :: ai :: aim :: AITR-791|
From the bitsavers.org collection, a scanned-in computer-related document.mit :: ai :: aim :: AITR-791
Keywords: intersection; configuration; constraints; algorithm; space; rotational; path; applicability; constraint; rotation; applicability constraints; solution path; configuration space; motion planning; intersection manifold; geometric planning; applicability set; moving object; intersection manifolds; three dimensional
|mit :: ai :: aim :: AITR-982|
From the bitsavers.org collection, a scanned-in computer-related document.mit :: ai :: aim :: AITR-982
Keywords: edr; motion; configuration; projection; space; sensing; robot; generalized; algorithm; termination; edr region; motion planning; configuration space; edr strategy; generalized configuration; model error; failure mode; start region; forward projection; termination predicate
|mit :: ai :: aim :: AIM-883|
From the bitsavers.org collection, a scanned-in computer-related document.mit :: ai :: aim :: AIM-883
Keywords: configuration; planner; constraints; objects; motions; obstacle; object; moving; planning; constraint; single moving; obstacle polygon; configuration space; planning object; moving objects; multiple moving; time complexity; constraints imposed; moving object; motion planning
|MIT2.12F04 - MIT OpenCourseWare|
This course provides an overview of robot mechanisms, dynamics, and intelligent controls. Topics include planar and spatial kinematics, and motion planning; mechanism design for manipulators and mobile robots, multi-rigid-body dynamics, 3D graphic simulation; control design, actuators, and sensors; wireless networking, task modeling, human-machine interface, and embedded software. Weekly laboratories provide experience with servo drives, real-time control, and embedded software...
Keywords: robot; robot design; rescue; recovery; automation; dynamics; statics; intelligent control; planar and spatial kinematics; motion planning; manipulator; mobile robots; multi-rigid-body dynamics; 3D graphic simulation; control design; actuator; sensor; task modeling; human-machine interface; embedded software; servo; servomechanism; real-time control; computer vision; navigation; tele-robotics; virtual reality